Connectionist Natural Language Processing

Connection science is a new information-processing paradigm which attempts to imitate the architecture and process of the brain, and brings together researchers from disciplines as diverse as computer science, physics, psychology, philosophy, linguistics, biology, engineering, neuroscience and AI. Work in Connectionist Natural Language Processing (CNLP) is now expanding rapidly, yet much of the work is still only available in journals, some of them quite obscure. To make this research more accessible this book brings together an important and comprehensive set of articles from the journal CONNECTION SCIENCE which represent the state of the art in Connectionist natural language processing; from speech recognition to discourse comprehension. While it is quintessentially Connectionist, it also deals with hybrid systems, and will be of interest to both theoreticians as well as computer modellers. Range of topics covered: Connectionism and Cognitive Linguistics Motion, Chomskys Government-binding Theory Syntactic Transformations on Distributed Representations Syntactic Neural Networks A Hybrid Symbolic/Connectionist Model for Understanding of Nouns Connectionism and Determinism in a Syntactic Parser Context Free Grammar Recognition Script Recognition with Hierarchical Feature Maps Attention Mechanisms in Language Script-Based Story Processing A Connectionist Account of Similarity in Vowel Harmony Learning Distributed Representations Connectionist Language Users Representation and Recognition of Temporal Patterns A Hybrid Model of Script Generation Networks that Learn about Phonological Features Pronunciation in Text-to-Speech Systems

[1]  James A. Anderson,et al.  Syntactic category disambiguation with neural networks , 1989 .

[2]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[3]  R. Taraban,et al.  Language learning: Cues or rules? , 1989 .

[4]  Yorick Wilks,et al.  Theoretical Issues in Natural Language Processing , 2018 .

[5]  James L. McClelland,et al.  An interactive activation model of context effects in letter perception: I. An account of basic findings. , 1981 .

[6]  Idan Segev,et al.  Methods in neuronal modeling: From synapses to networks , 1989 .

[7]  Noam Chomsky,et al.  वाक्यविन्यास का सैद्धान्तिक पक्ष = Aspects of the theory of syntax , 1965 .

[8]  Gary S. Dell,et al.  The retrieval of phonological forms in production: tests of predictions from a connectionist model , 1988 .

[9]  S Pinker,et al.  Rules of language. , 1991, Science.

[10]  W. Lehnert.  Possible implications of connectionism , 1987, TINLAP '87.

[11]  James L. McClelland,et al.  Learning and Applying Contextual Constraints in Sentence Comprehension , 1990, Artif. Intell..

[12]  Karen Kukich,et al.  Where do Phrases Come from: Some Preliminary Experiments in Connectionist Phrase Generation , 1987 .

[13]  R. Reilly,et al.  Connectionist approaches to natural language processing , 1994 .

[14]  G. Marcus,et al.  Regular and irregular inflection in the acquisition of German noun plurals , 1992, Cognition.

[15]  Jeffrey L. Elman,et al.  Distributed Representations, Simple Recurrent Networks, and Grammatical Structure , 1991, Mach. Learn..

[16]  Trevor A. Harley,et al.  Phonological activation of semantic competitors during lexical access in speech production , 1993 .

[17]  J. Fodor,et al.  Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.

[18]  Terrence J. Sejnowski,et al.  Parallel Networks that Learn to Pronounce English Text , 1987, Complex Syst..

[19]  James L. McClelland,et al.  Connections and disconnections: Acquired dyslexia in a computational model of reading processes. , 1989 .

[20]  James L. McClelland,et al.  A distributed, developmental model of word recognition and naming. , 1989, Psychological review.

[21]  J. Elman.  Learning and development in neural networks: the importance of starting small , 1993, Cognition.

[22]  N. Chater,et al.  Autonomy, implementation and cognitive architecture: A reply to Fodor and Pylyshyn , 1990, Cognition.

[23]  A. Turing.  On Computable Numbers, with an Application to the Entscheidungsproblem. , 1937 .

[24]  A. H. Kawamoto.  Distributed Representations of Ambiguous Words and Their Resolution in a Connectionist Network , 1988 .

[25]  Nick Chater,et al.  A phonologically motivated input representation for the modelling of auditory word perception in continuous speech , 1992 .

[26]  J. Elman.  Representation and structure in connectionist models , 1991 .

[27]  A. A. Mullin,et al.  Principles of neurodynamics , 1962 .

[28]  Eugene Charniak,et al.  Statistical language learning , 1997 .

[29]  N. E. Sharkey,et al.  A PDP learning approach to natural language understanding , 1989 .

[30]  Jeffrey L. Elman,et al.  Interactive processes in speech perception: the TRACE model , 1986 .

[31]  Noel E. Sharkey,et al.  Implementing Soft Preferences for Structural Disambiguation , 1990, ÖGAI.

[32]  James L. McClelland,et al.  An interactive activation model of context effects in letter perception: Part 2. The contextual enhancement effect and some tests and extensions of the model. , 1982, Psychological review.

[33]  Garrison W. Cottrell,et al.  A connectionist approach to word sense disambiguation , 1988 .

[34]  W. Levelt.  Speaking: From Intention to Articulation , 1990 .

[35]  W. Ashby.  Design for a Brain , 1954 .

[36]  Jerome A. Feldman,et al.  Connectionist Models and Their Properties , 1982, Cogn. Sci..

[37]  James L. McClelland,et al.  Mechanisms of Sentence Processing: Assigning Roles to Constituents of Sentences , 1986 .

[38]  James L. McClelland,et al.  Cognitive penetration of the mechanisms of perception: Compensation for coarticulation of lexically restored phonemes , 1988 .

[39]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[40]  Jordan B. Pollack,et al.  Massively Parallel Parsing: A Strongly Interactive Model of Natural Language Interpretation , 1988, Cogn. Sci..

[41]  Mark A. Fanty,et al.  Context-free parsing with connectionist networks , 1987 .

[42]  Michael G. Dyer,et al.  High-level Inferencing in a Connectionist Network , 1989 .

[43]  Paul Smolensky,et al.  Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1990, Artif. Intell..

[44]  Noel E. Sharkey,et al.  Mixing Binary and Continuous Connection Schemes for Knowledge Access , 1986, AAAI.

[45]  Tim van Gelder,et al.  Compositionality: A Connectionist Variation on a Classical Theme , 1990, Cogn. Sci..

[46]  J. Fodor.  The Modularity of mind. An essay on faculty psychology , 1986 .

[47]  Cynthia L. Cosic,et al.  Learning to Represent and Understand Locative Prepositional Phrases , 1988 .

[48]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[49]  Jordan B. Pollack,et al.  Recursive Distributed Representations , 1990, Artif. Intell..

[50]  V. Marchman,et al.  U-shaped learning and frequency effects in a multi-layered perception: Implications for child language acquisition , 1991, Cognition.

[51]  P. Smolensky.  On the proper treatment of connectionism , 1988, Behavioral and Brain Sciences.

[52]  Noam Chomsky.  Knowledge of Language , 1986 .

[53]  S. Pinker,et al.  On language and connectionism: Analysis of a parallel distributed processing model of language acquisition , 1988, Cognition.

[54]  James L. McClelland,et al.  Generalization with Componential Attractors: Word and Nonword Reading in an Attractor Network , 1993 .

[55]  Gary S. Dell,et al.  Structure and Content in Language Production: A Theory of Frame Constraints in Phonological Speech Errors , 1993, Cogn. Sci..